한국기계가공학회지 (Journal of the Korean Society of Manufacturing Process Engineers)

  • 제21권2호
  • /
  • Pages.137-143
  • /
  • 2022
  • /
  • 1598-6721(pISSN)
  • /
  • 2288-0771(eISSN)
한국기계가공학회 (The Korean Society of Manufacturing Process Engineers)
권호 표지
DOI QR코드

DOI QR Code

3D 프린팅 복합소재의 가공에서 가공 조건 선정을 위한 머신러닝 개발에 관한 연구

Development of Machine Learning Method for Selection of Machining Conditions in Machining of 3D Printed Composite Material

  • 김민재 (창원대학교 스마트제조융합협동과정) ;
  • 김동현 (창원대학교 메카트로닉스연구원) ;
  • 이춘만 (창원대학교 기계공학부)
  • Kim, Min-Jae (School of Smart Manufacturing Engineering, Changwon National University) ;
  • Kim, Dong-Hyeon (Mechatronics Research Center, Changwon National University) ;
  • Lee, Choon-Man (Department of Mechanical Engineering, Changwon National University)
  • 투고 : 2021.11.19
  • 심사 : 2021.12.07
  • 발행 : 2022.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Composite materials, being light-weight and of high mechanical strength, are increasingly used in various industries such as the aerospace, automobile, sporting-goods manufacturing, and ship-building industries. Recently, manufacturing of composite materials using 3D printers has increased. 3D-printed composite materials are made in free-form and adapted for end-use by adjusting the fiber content and orientation. However, research on the machining of 3D printed composite materials is limited. The aim of this study is to develop a machine learning method to select machining conditions for machining of 3D-printed composite materials. The composite material was composed of Onyx and carbon fibers and stacked sequentially. The experiments were performed using the following machining conditions: spindle speed, feed rate, depth of cut, and machining direction. Cutting forces of the different machining conditions were measured by milling the composite materials. PCA, a method of machine learning, was developed to select the machining conditions and will be used in subsequent experiments under various machining conditions.

키워드

과제정보

이 논문은 2021~2022년도 창원대학교 자율연구과제 연구비 지원으로 수행된 연구결과임.

참고문헌

  1. Kim, M. S., Lee, M. K., Cho, G. H. and Lee, S. K., "Effect of the Fiber Orientation and the Radial Depth of Cut on the Flank Wear in the Milling of CFRP," International Journal of Precision Engineering and Manufacturing, Vol. 21, pp. 1187-1199, 2020. https://doi.org/10.1007/s12541-020-00340-w
  2. Sa, M. W., "Comparison Study on Side Milling of CFRP with AlcrN-based, Diamond-Like-Carbon(DLC), and Diamond-Coated End Mill," Journal of the Korean Society of Manufacturing Process Engineers, Vol. 19, pp. 9-15, 2020.
  3. Choi, M. S., "Statistical Analysis of Cutting Force for End Milling with Different Cutting Tool Materials," Journal of the Semiconductor & Display Technology, Vol. 15, pp. 86-91, 2016.
  4. He, Y. L., Qing, H. A., Zhang, S. G., Wang, D. Z., and Zhu, S. W., "The cutting force and defect analysis in milling of carbon fiber-reinforced polymer (CFRP) composite," The International Jounal of Abvanced Manufactruing Technology, Vol. 93, pp. 1829-1842, 2017. https://doi.org/10.1007/s00170-017-0613-6
  5. Halim, N. F. H. A., Ascroft, H. and Barnes, S., "Analysis of Tool Wear, Cutting Force, Surface Roughness and Machining Temperature During Finishing Operation of Ultrasonic Assisted Milling (UAM) of Carbon Fiber Reinforced Plastic (CFRP)," Procedia Engineering, Vol. 184, pp.185-191, 2017. https://doi.org/10.1016/j.proeng.2017.04.084
  6. Ekoi, E. J., Dickson, A. N., Dowling, D. P., "Investigating the fatigue and mechanical behavior of 3D printed woven and nonwoven continuous carbon fiber reinforced polymer(CFRP) composites," Composites Part B: Engineering, Vol. 212, No. 1, 2021.
  7. Hagino, M. and Inoue, T., "Effect of Carbon Fiber Orientation and Helix Angle on CFRP Cutting Characteristics by End-milling," International Journal of Automation Technology, Vol. 7, No. 3, pp. 292-299, 2013. https://doi.org/10.20965/ijat.2013.p0292
  8. Yoo, Y. S., Kim, D. H., Kim, S. and Hur, J. W., "Fault Prognostics of a SMPS based on PCA-SVM," Journal of the Korean Society of Manufacturing Process Engineers, Vol. 19, No. 9, pp. 47-52, 2020.